Project Abstract Far too many people have personal experience with the destructive nature of Alzheimer's disease (AD). Despite significant progress identifying genetic risk factors and increased understanding of the inflammatory and immune response in AD etiology, our knowledge remains inadequate to develop effective preventions or cures. We have linked data and subjects from the Utah Population Database (medical records, death certificates, and genealogy for over 7 million subjects) and the Cache County Study on Memory in Aging (longitudinal cognitive assessment on over 5,000 subjects in Utah). These samples are an accurate representation of the general European American population, making findings from these data generalizable in that context. The combination of these studies enables the execution of an innovative design for gene discovery, and to evaluate the association between AD risk and resilience pedigrees, and key aspects of AD epidemiology, including socioeconomic status, cardiovascular disease, cancer and many others. We will first, conduct studies that leverage linkage and association to identify novel genetic risk factors. Second, we will use the UPDB to conduct powerful studies of measurable risk and resilience factors for AD. Third, we will collect additional samples from key pedigrees to enhance our study. And finally, all data associated with our effort will be harmonized and deposited into public databases. In summary our approach is carefully designed and well powered to provide new knowledge and facilitate efforts to develop a cure for AD. Specifically, we will augment the Cache County Study, an existing longitudinal cohort study, in an efficient and directed manner, including collecting and sequencing DNA samples from well-characterized cases and controls in the study. Using our unparalleled and powerful dataset and approach, we will explore trends in the risk of AD and their explanation via putative risk and protective factors. Our successful efforts will identify measurable risk and resilience factors for AD, enabling precision medicine by providing information for modifying risk in individuals and providing insights into those who will benefit most from therapeutic interventions. Finally, all data in from this proposal will be harmonized with relevant datasets and electronically archived in appropriate databases.